We follow the setup of [1]} and compare against their baselines, which use MSE prediction error in feature space as the intrinsic motivation signal, aka surprisal in feature space. The feature space is obtained by projecting states from the environment into a lower-dimensional space using a feature model, i.e. next-state features can be expressed as \(\phi _{t+1} = f(s_{t+1})\) .
